STA100 Applied Statistics for the Biological Science

These flashcards cover terminology and key concepts from the STA100 Applied Statistics for the Biological Science lecture.

Descriptive Statistics

The branch of statistics that deals with the summarization and description of data.

Probability

A measure of the likelihood that an event will occur.

Sampling Distributions

The probability distribution of a statistic obtained by selecting random samples from a population.

Hypothesis Testing

A method for testing a claim or hypothesis about a parameter in a population using data.

Contingency Tables

A table used to display the frequency distribution of variables.

ANOVA

Analysis of variance, a statistical method used to test differences between two or more means.

Regression

A statistical process for estimating the relationships among variables.

Experimental Study

Research that seeks to determine cause and effect relationships by manipulating variables.

Observational Study

A study in which the researcher observes and measures behavior without intervening.

Anecdotal Evidence

Evidence based on personal accounts or stories rather than on scientific data.

Bioconductor

A project that provides tools for the analysis of genomic data using the R programming language.

End Points

Outcomes measured in a clinical trial or study to assess the effectiveness of a treatment.

Confounding Variable

An extraneous variable that influences both the dependent variable and independent variable.

Independent Variable

The variable that is changed or controlled in a scientific experiment.

Dependent Variable

The variable observed and measured for change in an experiment.

subject

person, place or thing we measure data from

variable

a characteristic that describes the subjects

factor

a controlled independent variable

treatment

something that researchers administer to experimental units

experimental unit

smallest unit to which a treatment is applied

placebo

a neutral treatment with no active effect on the response variable

placebo effect

a subject’s response caused by expectation

blinding

the treatment assignment is kept secret from the subject

double blinding

when both subject and researchers are blinded

random variable

a variable whose outcome is the result of random process and can’t be predicted

(r.v. or RV)

population

collection of all subjects of interest (often unmesaurable)

sample

subset of the population

n

size of the sample

simple random sample

sample where all units have equal chance of being selected

types of variable

1) categorical variables: variable measured as labels (qualitative labels)

• nominal: labels w/ no natural order

  • ex: major, blood type, color, or cancer outcome

• ordinal: labels w/ natural order

  • ex: such as degree level or stage of cancer

2) numerical variables (quantitative variables): variables that are recorded as numbers

• continuous: values take on any value in an interval

  • ex: temperature, height, weight, blood pressure [1.2,2]

• Discrete: numbers w/ natural gaps, often integers

  • age in whole years, number of objects

Y

the set all possible values of a R.V.

• ex: let Y be a roll of die then Y={1,2,3,4,5,6}

Y_i

the ith value of R.V. Y and itself still random before measuring

• ex: Y_i is value of the ith roll of the die. Y₅ is the value of the 5th roll of the die not the value of 5 itself

y

denotes one specific value of a R.V.

y_i

the ith observed value of R.V. Y after measuring, also called the observation

ex: y₃=2